Balz Maag, Phd Student ETH Zürich Betreuerin: Olga Saukh - 14:00 Uhr
Low-cost environmental sensors, such as barometers or air quality sensors, have shown great potential for a large variety of sensing applications.
Especially wearables equipped with different environmental sensors offer interesting use-cases for personal and participatory sensing.
This talk addresses two specific applications of environmental sensors in wearables:
1) "BARTON: Low Power Tongue Movement Sensing with In-ear Barometers":
Sensing tongue movements enables various applications in hands-free interaction and alternative communication.
We propose BARTON, a BARometer based low-power and robust TONgue movement sensing system.
Using a low sampling rate of below 50 Hz, and only extracting simple temporal features from in-ear pressure signals, we demonstrate that it is plausible to distinguish important tongue gestures (left, right, forward) at low power consumption.
2) "W-Air: Enabling Personal Air Pollution Monitoring on Wearables"
Accurate, portable and personal air pollution sensing devices enable quantification of individual exposure to air pollution, personalized health advice and assistance applications.
Wearables (e.g., on wristbands, attached to belts or backpacks) are promising to integrate commercial off-the-shelf gas sensors for personal air pollution sensing.
Yet previous research lacks comprehensive investigations on the accuracies of air pollution sensing on wearables.
In response, we proposed W-Air, an accurate personal multi-pollutant monitoring platform for wearables.
We discovered that human emissions introduce non-linear interference when low-cost gas sensors are integrated into wearables, which is overlooked in existing studies.
W-Air adopts a sensor-fusion calibration scheme to recover high-fidelity ambient pollutant concentrations from the human interference.
Balz Maag received his M.Sc. degree in electrical engineering and information technology from ETH Zurich in 2014.
He is currently a Ph.D. student in the Computer Engineering and Networks Laboratory (TIK) at ETH Zurich.
His research interests include the development and optimization of algorithms for wireless sensor networks and crowd-sensing applications with a special focus on sensor calibration.